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Anesthesiologists in the operating room are unable to constantly monitor all data generated by physiological monitors. They are further distracted by clinical and educational tasks. An expert system would ideally provide assistance to the anesthesiologist in this data-rich environment. Clinical monitoring expert systems have not been widely adopted, as traditional methods of knowledge encoding require both expert medical and programming skills, making knowledge acquisition difficult. A software application was developed for use as a knowledge authoring tool for physiological monitoring. This application enables clinicians to create knowledge rules without the need of a knowledge engineer or programmer. These rules are designed to provide clinical diagnosis, explanations and treatment advice for optimal patient care to the clinician in real time. By intelligently combining data from physiological monitors and demographical data sources the expert system can use these rules to assist in monitoring the patient. The knowledge authoring process is simplified by limiting connective relationships between rules. The application is designed to allow open collaboration between communities of clinicians to build a library of rules for clinical use. This design provides clinicians with a system for parameter surveillance and expert advice with a transparent pathway of reasoning. A usability evaluation demonstrated that anesthesiologists can rapidly develop useful rules for use in a predefined clinical scenario. [ABSTRACT FROM AUTHOR]

Several triage systems have been developed, but little is known about their performance in low-resource settings. Evaluating and comparing novel triage systems to existing triage scales provides essential information about their added value, reliability, safety, and effectiveness before adoption. This study included children aged < 15 years who presented to the emergency departments of two public hospitals in Kenya between February and December 2021. We compared the performance of Emergency Triage Assessment and Treatment (ETAT) guidelines and Smart Triage (ST) models (ST model with independent triggers, and recalibrated ST model with independent triggers) in categorizing children into emergency, priority, and non-urgent triage categories. Sankey diagrams were used to visualize the distribution of children into similar or different triage categories by ETAT and ST models. Sensitivity, specificity, negative and positive predictive values for mortality and admission were calculated. 5618 children were enrolled, and the majority (3113, 55.4%) were aged between one and five years of age. Overall admission and mortality rates were 7% and 0.9%, respectively. ETAT classified 513 (9.2%) children into the emergency category compared to 1163 (20.8%) and 1161 (20.7%) by the ST model with independent triggers and recalibrated model with independent triggers, respectively. ETAT categorized 3089 (55.1%) children as non-urgent compared to 2097 (37.4%) and 2617 (46.7%) for the respective ST models. ETAT classified 191/395 (48.4%) admitted patients as emergencies compared to more than half by all the ST models. ETAT and ST models classified 25/49 (51%) and 39/49 (79.6%) deceased children as emergencies. Sensitivity for admission and mortality was 48.4% and 51% for ETAT and 74.9% and 79.6% for the ST models, respectively. Smart Triage shows potential for identifying critically ill children in low-resource settings, particularly when combined with independent triggers and performs comparably to ETAT. Evaluation of Smart Triage in other contexts and comparison to other triage systems is required., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kamau et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Infectious diseases in neonates account for half of the under-five mortality in low- and middle-income countries. Data-driven algorithms such as clinical prediction models can be used to efficiently detect critically ill children in order to optimize care and reduce mortality. Thus far, only a handful of prediction models have been externally validated and are limited to neonatal in-hospital mortality. The aim of this study is to externally validate a previously derived clinical prediction model (Smart Triage) using a combined prospective baseline cohort from Uganda and Kenya with a composite endpoint of hospital admission, mortality, and readmission. We evaluated model discrimination using area under the receiver-operator curve (AUROC) and visualized calibration plots with age subsets (< 30 days, ≤ 2 months, ≤ 6 months, and < 5 years). Due to reduced performance in neonates (< 1 month), we re-estimated the intercept and coefficients and selected new thresholds to maximize sensitivity and specificity. 11595 participants under the age of five (under-5) were included in the analysis. The proportion with an endpoint ranged from 8.9% in all children under-5 (including neonates) to 26% in the neonatal subset alone. The model achieved good discrimination for children under-5 with AUROC of 0.81 (95% CI: 0.79-0.82) but poor discrimination for neonates with AUROC of 0.62 (95% CI: 0.55-0.70). Sensitivity at the low-risk thresholds (CI) were 85% (83%-87%) and 68% (58%-76%) for children under-5 and neonates, respectively. After model revision for neonates, we achieved an AUROC of 0.83 (95% CI: 0.79-0.87) with 13% and 41% as the low- and high-risk thresholds, respectively. The updated Smart Triage performs well in its predictive ability across different age groups and can be incorporated into current triage guidelines at local healthcare facilities. Additional validation of the model is indicated, especially for the neonatal model., Competing Interests: John Mark Ansermino serves as a section editor for PLOS Digital Health., (Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Models for digital triage of sick children at emergency departments of hospitals in resource poor settings have been developed. However, prior to their adoption, external validation should be performed to ensure their generalizability. We externally validated a previously published nine-predictor paediatric triage model (Smart Triage) developed in Uganda using data from two hospitals in Kenya. Both discrimination and calibration were assessed, and recalibration was performed by optimizing the intercept for classifying patients into emergency, priority, or non-urgent categories based on low-risk and high-risk thresholds. A total of 2539 patients were eligible at Hospital 1 and 2464 at Hospital 2, and 5003 for both hospitals combined; admission rates were 8.9%, 4.5%, and 6.8%, respectively. The model showed good discrimination, with area under the receiver-operator curve (AUC) of 0.826, 0.784 and 0.821, respectively. The pre-calibrated model at a low-risk threshold of 8% achieved a sensitivity of 93% (95% confidence interval, (CI):89%-96%), 81% (CI:74%-88%), and 89% (CI:85%-92%), respectively, and at a high-risk threshold of 40%, the model achieved a specificity of 86% (CI:84%-87%), 96% (CI:95%-97%), and 91% (CI:90%-92%), respectively. Recalibration improved the graphical fit, but new risk thresholds were required to optimize sensitivity and specificity.The Smart Triage model showed good discrimination on external validation but required recalibration to improve the graphical fit of the calibration plot. There was no change in the order of prioritization of patients following recalibration in the respective triage categories. Recalibration required new site-specific risk thresholds that may not be needed if prioritization based on rank is all that is required. The Smart Triage model shows promise for wider application for use in triage for sick children in different settings., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kigo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In many low-income countries, over five percent of hospitalized children die following hospital discharge. The lack of available tools to identify those at risk of post-discharge mortality has limited the ability to make progress towards improving outcomes. We aimed to develop algorithms designed to predict post-discharge mortality among children admitted with suspected sepsis. Four prospective cohort studies of children in two age groups (0-6 and 6-60 months) were conducted between 2012-2021 in six Ugandan hospitals. Prediction models were derived for six-months post-discharge mortality, based on candidate predictors collected at admission, each with a maximum of eight variables, and internally validated using 10-fold cross-validation. 8,810 children were enrolled: 470 (5.3%) died in hospital; 257 (7.7%) and 233 (4.8%) post-discharge deaths occurred in the 0-6-month and 6-60-month age groups, respectively. The primary models had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95%CI 0.74-0.80) for 0-6-month-olds and 0.75 (95%CI 0.72-0.79) for 6-60-month-olds; mean AUROCs among the 10 cross-validation folds were 0.75 and 0.73, respectively. Calibration across risk strata was good: Brier scores were 0.07 and 0.04, respectively. The most important variables included anthropometry and oxygen saturation. Additional variables included: illness duration, jaundice-age interaction, and a bulging fontanelle among 0-6-month-olds; and prior admissions, coma score, temperature, age-respiratory rate interaction, and HIV status among 6-60-month-olds. Simple prediction models at admission with suspected sepsis can identify children at risk of post-discharge mortality. Further external validation is recommended for different contexts. Models can be digitally integrated into existing processes to improve peri-discharge care as children transition from the hospital to the community., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wiens et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: In low- and middle-income countries, health workers use pulse oximeters for intermittent spot measurements of oxygen saturation (SpO2). However, the accuracy and reliability of pulse oximeters for spot measurements have not been determined. We evaluated the repeatability of spot measurements and the ideal observation time to guide recommendations during spot check measurements., Methods: Two 1-minute measurements were taken for the 3,903 subjects enrolled in the study conducted April 2020-January 2022 in Uganda, collecting 1 Hz SpO2 and signal quality index (SQI) data. The repeatability between the 2 measurements was assessed using an intraclass correlation coefficient (ICC), calculated using a median of all seconds of non-zero SpO2 values for each recording (any quality, Q1) and again with a quality filter only using seconds with SQI 90% or higher (good quality, Q2). The ICC was also recalculated for both conditions of Q1 and Q2 using the initial 5 seconds, then the initial 10 seconds, and continuing with 5-second increments up to the full 60 seconds. Lastly, the whole minute ICC was calculated with good quality (Q2), including only records where both measurements had a mean SQI of more than 70% (Q3)., Results: The repeatability ICC with condition Q1 was 0.591 (95% confidence interval [CI]=0.570, 0.611). Using only the first 5 seconds of each measurement reduced the repeatability to 0.200 (95% CI=0.169, 0.230). Filtering with Q2, the whole-minute ICC was 0.855 (95% CI=0.847, 0.864). The ICC did not improve beyond the first 35 seconds. For Q3, the repeatability rose to 0.908 (95% CI=0.901, 0.914)., Conclusions: Training guidelines must emphasize the importance of signal quality and duration of measurement, targeting a minimum of 35 seconds of adequate-quality, stable data. In addition, the design of new devices should incorporate user prompts and force quality checks to encourage more accurate pulse oximetry measurements., (© Asdo et al.)

Background: Substantial mortality occurs after hospital discharge in children younger than 5 years with suspected sepsis, especially in low-income countries. A better understanding of its epidemiology is needed for effective interventions to reduce child mortality in these countries. We evaluated risk factors for death after discharge in children admitted to hospital for suspected sepsis in Uganda, and assessed how these differed by age, time of death, and location of death., Methods: In this prospective, multisite, observational cohort study, we recruited and consecutively enrolled children aged 0-60 months admitted with suspected sepsis from the community to the paediatric wards of six Ugandan hospitals. Suspected sepsis was defined as the need for admission due to a suspected or proven infectious illness. At admission, trained study nurses systematically collected data on clinical variables, sociodemographic variables, and baseline characteristics with encrypted study tablets. Participants were followed up for 6 months after discharge by field officers who contacted caregivers at 2 months and 4 months after discharge by telephone and at 6 months after discharge in person to measure vital status, health-care seeking after discharge, and readmission details. We assessed 6-month mortality after hospital discharge among those discharged alive, with verbal autopsies conducted for children who had died after hospital discharge., Findings: Between July 13, 2017, and March 30, 2020, 16 991 children were screened for eligibility. 6545 children (2927 [44·72%] female children and 3618 [55·28%] male children) were enrolled and 6191 were discharged from hospital alive. 6073 children (2687 [44·2%] female children and 3386 [55·8%] male children) completed follow-up. 366 children died in the 6-month period after discharge (weighted mortality rate 5·5%). Median time from discharge to death was 28 days (IQR 9-74). For the 360 children for whom location of death was documented, deaths occurred at home (162 [45·0%]), in transit to care (66 [18·3%]), or in hospital (132 [36·7%]) during a subsequent readmission. Death after hospital discharge was strongly associated with weight-for-age Z scores less than -3 (adjusted risk ratio [aRR] 4·7, 95% CI 3·7-5·8 vs a Z score of >-2), discharge or referral to a higher level of care (7·3, 5·6-9·5), and unplanned discharge (3·2, 2·5-4·0). Hazard ratios (HRs) for severe anaemia (<7g/dL) increased with time since discharge, from 1·7 (95% CI 0·9-3·0) for death occurring in the first time tertile to 5·2 (3·1-8·5) in the third time tertile. HRs for some discharge vulnerabilities decreased significantly with increasing time since discharge, including unplanned discharge (from 4.5 [2·9-6·9] in the first tertile to 2·0 [1·3-3·2] in the third tertile) and poor feeding status (from 7·7 [5·4-11·0] to 1·84 [1·0-3·3]). Age interacted with several variables, including reduced weight-for-age Z score, severe anaemia, and reduced admission temperature., Interpretation: Paediatric mortality following hospital discharge after suspected sepsis is common, with diminishing, although persistent, risk during the first 6 months after discharge. Efforts to improve outcomes after hospital discharge are crucial to achieving Sustainable Development Goal 3.2 (ending preventable childhood deaths under age 5 years)., Funding: Grand Challenges Canada, Thrasher Research Fund, BC Children's Hospital Foundation, and Mining4Life., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background : The success of many machine learning applications depends on knowledge about the relationship between the input data and the task of interest (output), hindering the application of machine learning to novel tasks. End-to-end deep learning, which does not require intermediate feature engineering, has been recommended to overcome this challenge but end-to-end deep learning models require large labelled training data sets often unavailable in many medical applications. In this study, we trained self-supervised learning (SSL) models for automatic feature extraction from raw photoplethysmography (PPG) obtained using a pulse oximeter, with the aim of predicting paediatric hospitalization.  Methods : We compared logistic regression models fitted using features extracted using SSL with models trained using both clinical and SSL features. In addition, we compared end-to-end deep learning models initialized randomly or using weights from the SSL models. We also compared the performance of SSL models trained on labelled data alone (n=1,031) with SSL trained using both labelled and unlabelled signals (n=7,578). Results : Logistic regression models were more predictive of hospitalization when trained on features extracted using labelled PPG signals only compared to SSL models trained on both labelled and unlabelled signals (AUC 0.83 vs 0.80). However, features extracted using SSL model trained on both labelled and unlabelled PPG signals were more predictive of hospitalization when concatenated with clinical features (AUC 0.89 vs 0.87). The end-to-end deep learning model had an AUC of 0.80 when initialized using the SSL model trained on all PPG signals, 0.77 when initialized using SSL trained on labelled data only, and 0.73 when initialized randomly. Conclusions : This study shows that SSL can extract features from PPG signals that are predictive of hospitalization or initialize end-to-end deep learning models. Furthermore, SSL can leverage larger unlabelled data sets to improve performance of models fitted using small labelled data sets., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Mwaniki P et al.)

Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8-18.9%) to 28.1% (IQR 23.5-36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were - 0.5 (- 2.7, 1.66), - 3.16 (- 12.12, 5.8), and - 3.99 (- 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing., (© 2022. The Author(s).)

Introduction: Early and accurate recognition of children at risk of progressing to critical illness could contribute to improved patient outcomes and resource allocation. In resource limited settings digital triage tools can support decision making and improve healthcare delivery. We developed a model for rapid identification of critically ill children at triage., Methods: This was a prospective cohort study of acutely ill children presenting at Jinja Regional Referral Hospital in Eastern Uganda. Variables collected in the emergency department informed the development of a logistic model based on hospital admission using bootstrap stepwise regression. Low and high-risk thresholds for 90% minimum sensitivity and specificity, respectively generated three risk level categories. Performance was assessed using receiver operating characteristic curve analysis on a held-out test set generated by an 80:20 split with 10-fold cross validation. A risk stratification table informed clinical interpretation., Results: The model derivation cohort included 1,612 participants, with an admission rate of approximately 23%. The majority of admitted patients were under five years old and presenting with sepsis, malaria, or pneumonia. A 9-predictor triage model was derived: logit ( p ) = -32.888 + (0.252, square root of age) + (0.016, heart rate) + (0.819, temperature) + (-0.022, mid-upper arm circumference) + (0.048 transformed oxygen saturation) + (1.793, parent concern) + (1.012, difficulty breathing) + (1.814, oedema) + (1.506, pallor). The model afforded good discrimination, calibration, and risk stratification at the selected thresholds of 8% and 40%., Conclusion: In a low income, pediatric population, we developed a nine variable triage model with high sensitivity and specificity to predict who should be admitted. The triage model can be integrated into any digital platform and used with minimal training to guide rapid identification of critically ill children at first contact. External validation and clinical implementation are in progress., Competing Interests: NK serves as a Specialty Chief Editor for Frontiers in Pediatrics. The peer-review process was guided by an independent editor, and the authors have no other competing interests to declare., (© 2022 Mawji, Li, Dunsmuir, Komugisha, Novakowski, Wiens, Vesuvius, Kissoon and Ansermino.)

Background: Effective triage at hospitals can improve outcomes for children globally by helping identify and prioritize care for those most at-risk of death. Paper-based pediatric triage guidelines have been developed to support frontline health workers in low-resource settings, but these guidelines can be challenging to implement. Smart Triage is a digital triaging platform for quality improvement (QI) that aims to address this challenge. Smart Triage represents a major cultural and behavioural shift in terms of managing patients at health facilities in low-and middle-income countries. The purpose of this study is to understand user perspectives on the usability, feasibility, and acceptability of Smart Triage to inform ongoing and future implementation., Methods: This was a descriptive qualitative study comprising of face-to-face interviews with health workers (n = 15) at a regional referral hospital in Eastern Uganda, conducted as a sub-study of a larger clinical trial to evaluate Smart Triage (NCT04304235). Thematic analysis was used to assess the usability, feasibility, and acceptability of the platform, focusing on its use in stratifying and prioritizing patients according to their risk and informing QI initiatives implemented by health workers., Results: With appropriate training and experience, health workers found most features of Smart Triage usable and feasible to implement, and reported the platform was acceptable due to its positive impact on reducing the time to treatment for emergency pediatric cases and its use in informing QI initiatives within the pediatric ward. Several factors that reduced the feasibility and acceptability were identified, including high staff turnover, a lack of medical supplies at the hospital, and challenges with staff attitudes., Conclusion: Health workers can use the Smart Triage digital triaging platform to identify and prioritize care for severely ill children and improve quality of care at health facilities in low-resource settings. Future innovation is needed to address identified feasibility and acceptability challenges; however, this platform could potentially address some of the challenges to implementing current paper-based systems., (© 2022. The Author(s).)

Objective: Infectious diseases, including pneumonia, malaria, and diarrheal diseases, are the leading causes of death in children younger than 5 years worldwide. The vast majority of these deaths occur in resource-limited settings where there is significant variation in the availability and type of human, physical, and infrastructural resources. The ability to identity gaps in healthcare systems that may hinder their ability to deliver care is an important step to determining specific interventions for quality improvement. Our study objective was to develop a comprehensive, digital, open-access health facility survey to assess facility readiness to provide pediatric critical care in resource-limited settings (eg, low- and lower middle-income countries)., Methods: A literature review of existing facility assessment tools and global guidelines was conducted to generate a database of survey questions. These were then mapped to one of the following 8 domains: hospital statistics, services offered, operational flow, facility infrastructure, staff and training, medicines and equipment, diagnostic capacity, and quality of clinical care. A 2-phase survey was developed and an iterative review process of the survey was undertaken with 12 experts based in low- and middle-income countries. This was built into the REDCap Mobile Application for electronic data capture., Results: The literature review process yielded 7 facility assessment tools and 7 global guidelines for inclusion. After the iterative review process, the final survey consisted of 11 sections with 457 unique questions in the first phase, "environmental scan," focusing on the infrastructure, availability, and functionality of resources, and 3 sections with 131 unique questions in the second phase, "observation scan," focusing on the level of clinical competency., Conclusions: A comprehensive 2-phase survey was created to evaluate facility readiness for pediatric critical care. Results will assist hospital administrators and policymakers to determine priority areas for quality improvement, enabling them to implement a Plan-Do-Study-Act cycle to improve care for the critically ill child., Competing Interests: Disclosure: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw data sets, in part due to concerns regarding research participant confidentiality and privacy. Statistical data de-identification is an approach that can be used to preserve privacy and facilitate open data sharing. We have proposed a standardized framework for the de-identification of data generated from cohort studies in children in a low-and-middle income country. We applied a standardized de-identification framework to a data sets comprised of 241 health related variables collected from a cohort of 1750 children with acute infections from Jinja Regional Referral Hospital in Eastern Uganda. Variables were labeled as direct and quasi-identifiers based on conditions of replicability, distinguishability, and knowability with consensus from two independent evaluators. Direct identifiers were removed from the data sets, while a statistical risk-based de-identification approach using the k-anonymity model was applied to quasi-identifiers. Qualitative assessment of the level of privacy invasion associated with data set disclosure was used to determine an acceptable re-identification risk threshold, and corresponding k-anonymity requirement. A de-identification model using generalization, followed by suppression was applied using a logical stepwise approach to achieve k-anonymity. The utility of the de-identified data was demonstrated using a typical clinical regression example. The de-identified data sets was published on the Pediatric Sepsis Data CoLaboratory Dataverse which provides moderated data access. Researchers are faced with many challenges when providing access to clinical data. We provide a standardized de-identification framework that can be adapted and refined based on specific context and risks. This process will be combined with moderated access to foster coordination and collaboration in the clinical research community., Competing Interests: JMA serves as a section editor for PLOS Digital Health. The peer-review process was guided by an independent editor, and the authors have no other competing interests to declare., (Copyright: © 2022 Mawji et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Clinically feasible multiparameter continuous physiological monitoring technologies are needed for use in resource-constrained African healthcare facilities to allow for early detection of critical events and timely intervention for major morbidities in high-risk neonates. We conducted a prospective clinical feasibility study of a novel multiparameter continuous physiological monitoring technology in neonates at Pumwani Maternity Hospital in Nairobi, Kenya. To assess feasibility, we compared the performance of Sibel's Advanced Neonatal Epidermal (ANNE) technology to reference technologies, including Masimo's Rad-97 pulse CO-oximeter with capnography technology for heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO 2 ) measurements and Spengler's Tempo Easy non-contact infrared thermometer for temperature measurements. We evaluated key performance criteria such as up-time, clinical event detection performance, and the agreement of measurements compared to those from the reference technologies in an uncontrolled, real-world setting. Between September 15 and December 15, 2020, we collected and analyzed 503 h of ANNE data from 109 enrolled neonates. ANNE's up-time was 42 (11%) h more for HR, 77 (25%) h more for RR, and 6 (2%) h less for SpO 2 compared to the Rad-97. However, ANNE's ratio of up-time to total attached time was less than Rad-97's for HR (0.79 vs 0.86), RR (0.68 vs. 0.79), and SpO 2 (0.69 vs 0.86). ANNE demonstrated adequate performance in identifying high and low HR and RR and high temperature events; however, showed relatively poor performance for low SpO 2 events. The normalized spread of limits of agreement were 8.4% for HR and 52.2% for RR and the normalized root-mean-square deviation was 4.4% for SpO 2 . Temperature agreement showed a spread of limits of agreement of 2.8 °C. The a priori-identified optimal limits were met for HR and temperature but not for RR or SpO 2 . ANNE was clinically feasible for HR and temperature but not RR and SpO 2 as demonstrated by the technology's up-time, clinical event detection performance, and the agreement of measurements compared to those from the reference technologies., (© 2022. The Author(s).)

Background: Neonatal multiparameter continuous physiological monitoring (MCPM) technologies assist with early detection of preventable and treatable causes of neonatal mortality. Evaluating accuracy of novel MCPM technologies is critical for their appropriate use and adoption., Methods: We prospectively compared the accuracy of Sibel's Advanced Neonatal Epidermal (ANNE) technology with Masimo's Rad-97 pulse CO-oximeter with capnography and Spengler's Tempo Easy reference technologies during four evaluation rounds. We compared accuracy of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and skin temperature using Bland-Altman plots and root-mean-square deviation analyses (RMSD). Sibel's ANNE algorithms were optimized between each round. We created Clarke error grids with zones of 20% to aid with clinical interpretation of HR and RR results., Results: Between November 2019 and August 2020 we collected 320 hours of data from 84 neonates. In the final round, Sibel's ANNE technology demonstrated a normalized bias of 0% for HR and 3.1% for RR, and a non-normalized bias of -0.3% for SpO2 and 0.2°C for temperature. The normalized spread between 95% upper and lower limits-of-agreement (LOA) was 4.7% for HR and 29.3% for RR. RMSD for SpO2 was 1.9% and 1.5°C for temperature. Agreement between Sibel's ANNE technology and the reference technologies met the a priori-defined thresholds for 95% spread of LOA and RMSD. Clarke error grids showed that all HR and RR observations were within a 20% difference., Conclusion: Our findings suggest acceptable agreement between Sibel's ANNE and reference technologies. Clinical effectiveness, feasibility, usability, acceptability, and cost-effectiveness investigations are necessary for large-scale implementation., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Shuai Xu is Founder and Chief Executive Officer at Sibel Health; all other authors declare no competing interests. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Globally, 2.5 million neonates died in 2018, accounting for 46% of under-5 deaths. Multiparameter continuous physiological monitoring (MCPM) of neonates allows for early detection and treatment of life-threatening health problems. However, neonatal monitoring technology is largely unavailable in low-resource settings., Methods: In four evaluation rounds, we prospectively compared the accuracy of the EarlySense under-mattress device to the Masimo Rad-97 pulse CO-oximeter with capnography reference device for heart rate (HR) and respiratory rate (RR) measurements in neonates in Kenya. EarlySense algorithm optimisations were made between evaluation rounds. In each evaluation round, we compared 200 randomly selected epochs of data using Bland-Altman plots and generated Clarke error grids with zones of 20% to aid in clinical interpretation., Results: Between 9 July 2019 and 8 January 2020, we collected 280 hours of MCPM data from 76 enrolled neonates. At the final evaluation round, the EarlySense MCPM device demonstrated a bias of -0.8 beats/minute for HR and 1.6 breaths/minute for RR, and normalised spread between the 95% upper and lower limits of agreement of 6.2% for HR and 27.3% for RR. Agreement between the two MCPM devices met the a priori-defined threshold of 30%. The Clarke error grids showed that all observations for HR and 197/200 for RR were within a 20% difference., Conclusion: Our research indicates that there is acceptable agreement between the EarlySense and Masimo MCPM devices in the context of large within-subject variability; however, further studies establishing cost-effectiveness and clinical effectiveness are needed before large-scale implementation of the EarlySense MCPM device in neonates., Trial Registration Number: NCT03920761., Competing Interests: Competing interests: YSH and RK are employed by EarlySense. All other authors declare no competing interests., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Multiparameter continuous physiological monitoring (MCPM) technologies are critical in the clinical management of high-risk neonates; yet, these technologies are frequently unavailable in many African healthcare facilities. We conducted a prospective clinical feasibility study of EarlySense's novel under-mattress MCPM technology in neonates at Pumwani Maternity Hospital in Nairobi, Kenya. To assess feasibility, we compared the performance of EarlySense's technology to Masimo's Rad-97 pulse CO-oximeter with capnography technology for heart rate (HR) and respiratory rate (RR) measurements using up-time, clinical event detection performance, and accuracy. Between September 15 and December 15, 2020, we collected and analyzed 470 hours of EarlySense data from 109 enrolled neonates. EarlySense's technology's up-time per neonate was 2.9 (range 0.8, 5.3) hours for HR and 2.1 (range 0.9, 4.0) hours for RR. The difference compared to the reference was a median of 0.6 (range 0.1, 3.1) hours for HR and 0.8 (range 0.1, 2.9) hours for RR. EarlySense's technology identified high HR and RR events with high sensitivity (HR 81%; RR 83%) and specificity (HR 99%; RR 83%), but was less sensitive for low HR and RR (HR 0%; RR 14%) although maintained specificity (HR 100%; RR 95%). There was a greater number of false negative and false positive RR events than false negative and false positive HR events. The normalized spread of limits of agreement was 9.6% for HR and 28.6% for RR, which met the a priori-identified limit of 30%. EarlySense's MCPM technology was clinically feasible as demonstrated by high percentage of up-time, strong clinical event detection performance, and agreement of HR and RR measurements compared to the reference technology. Studies in critically ill neonates, assessing barriers and facilitators to adoption, and costing analyses will be key to the technology's development and potential uptake and scale-up., (© 2022. The Author(s).)

Objective . Investigation of the night-to-night (NtN) variability of pulse oximetry features in children with suspicion of Sleep Apnea. Approach . Following ethics approval and informed consent, 75 children referred to British Columbia Children's Hospital for overnight PSG were recorded on three consecutive nights, including one at the hospital simultaneously with polysomnography and 2 nights at home. During all three nights, a smartphone-based pulse oximeter sensor was used to record overnight pulse oximetry (SpO2 and photoplethysmogram). Features characterizing SpO2 dynamics and heart rate were derived. The NtN variability of these features over the three different nights was investigated using linear mixed models. Main results . Overall most pulse oximetry features (e.g. the oxygen desaturation index) showed no NtN variability. One of the exceptions is for the signal quality, which was significantly lower during at home measurements compared to measurements in the hospital. Significance . At home pulse oximetry screening shows an increasing predictive value to investigate obstructive sleep apnea (OSA) severity. Hospital recordings affect subjects normal sleep and OSA severity and recordings may vary between nights at home. Before establishing the role of home monitoring as a diagnostic test for OSA, we must first determine their NtN variability. Most pulse oximetry features showed no significant NtN variability and could therefore be used in future at-home testing to create a reliable and consistent OSA screening tool. A single night recording at home should be able to characterize pulse oximetry features in children., (Creative Commons Attribution license.)

Background: Heart rate (HR) and respiratory rate (RR) can be challenging to measure accurately and reliably in neonates. The introduction of innovative, non-invasive measurement technologies suitable for resource-constrained settings is limited by the lack of appropriate clinical thresholds for accuracy comparison studies. Methods: We collected measurements of photoplethysmography-recorded HR and capnography-recorded exhaled carbon dioxide across multiple 60-second epochs (observations) in enrolled neonates admitted to the neonatal care unit at Aga Khan University Hospital in Nairobi, Kenya. Trained study nurses manually recorded HR, and the study team manually counted individual breaths from capnograms. For comparison, HR and RR also were measured using an automated signal detection algorithm. Clinical measurements were analyzed for repeatability. Results: A total of 297 epochs across 35 neonates were recorded. Manual HR showed a bias of -2.4 (-1.8%) and a spread between the 95% limits of agreement (LOA) of 40.3 (29.6%) compared to the algorithm-derived median HR. Manual RR showed a bias of -3.2 (-6.6%) and a spread between the 95% LOA of 17.9 (37.3%) compared to the algorithm-derived median RR, and a bias of -0.5 (1.1%) and a spread between the 95% LOA of 4.4 (9.1%) compared to the algorithm-derived RR count. Manual HR and RR showed repeatability of 0.6 (interquartile range (IQR) 0.5-0.7), and 0.7 (IQR 0.5-0.8), respectively. Conclusions: Appropriate clinical thresholds should be selected a priori when performing accuracy comparisons for HR and RR. Automated measurement technologies typically use a smoothing or averaging filter, which significantly impacts accuracy. A wider spread between the LOA, as much as 30%, should be considered to account for the observed physiological nuances and within- and between-neonate variability and different averaging methods. Wider adoption of thresholds by data standards organizations and technology developers and manufacturers will increase the robustness of clinical comparison studies., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 Coleman J et al.)

Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age. Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers. Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation.  Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 Mawji A et al.)

Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age. Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers. Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation.  Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Mawji A et al.)

Conducting collaborative and comprehensive epidemiological research on neonatal sepsis in low- and middle-income countries (LMICs) is challenging due to a lack of diagnostic tests. This prospective study protocol aims to obtain epidemiological data on bacterial sepsis in newborns and young infants at Kamuzu Central Hospital in Lilongwe, Malawi. The main goal is to determine if the use of whole blood transcriptome host immune response signatures can help in the identification of infants who have sepsis of bacterial causes. The protocol includes a detailed clinical assessment with vital sign measurements, strict aseptic blood culture protocol with state-of-the-art microbial analyses and RNA-sequencing and metagenomics evaluations of host responses and pathogens, respectively. We also discuss the directions of a brief analysis plan for RNA sequencing data. This study will provide robust epidemiological data for sepsis in neonates and young infants in a setting where sepsis confers an inordinate burden of disease., Competing Interests: Competing interests: The investigators in this study are academic researchers in the public sector. Investigators in Malawi receive salary compensation for their time invested carrying this study. Many investigators on this study are clinicians involved in caring for sick babies and have a deep, vested interest in generating new knowledge as well as ensuring safety and well-being of research participants., (Copyright: © 2020 Popescu CR et al.)

Background: Sepsis is the leading cause of death in children under five in low- and middle-income countries. The rapid identification of the sickest children and timely antibiotic administration may improve outcomes. We developed and implemented a digital triage platform to rapidly identify critically ill children to facilitate timely intravenous antibiotic administration., Objective: This quality improvement initiative sought to reduce the time to antibiotic administration at a dedicated children's hospital outpatient department in Mbarara, Uganda., Intervention and Study Design: The digital platform consisted of a mobile application that collects clinical signs, symptoms, and vital signs to prioritize children through a combination of emergency triggers and predictive risk algorithms. A computer-based dashboard enabled the prioritization of children by displaying an overview of all children and their triage categories. We evaluated the impact of the digital triage platform over an 11-week pre-implementation phase and an 11-week post-implementation phase. The time from the end of triage to antibiotic administration was compared to evaluate the quality improvement initiative., Results: There was a difference of -11 minutes (95% CI, -16.0 to -6.0; p < 0.001; Mann-Whitney U test) in time to antibiotics, from 51 minutes (IQR, 27.0-94.0) pre-implementation to 44 minutes (IQR, 19.0-74.0) post-implementation. Children prioritized as emergency received the greatest time benefit (-34 minutes; 95% CI, -9.0 to -58.0; p < 0.001; Mann-Whitney U test). The proportion of children who waited more than an hour until antibiotics decreased by 21.4% (p = 0.007)., Conclusion: A data-driven patient prioritization and continuous feedback for healthcare workers enabled by a digital triage platform led to expedited antibiotic therapy for critically ill children with sepsis. This platform may have a more significant impact in facilities without existing triage processes and prioritization of treatments, as is commonly encountered in low resource settings., Competing Interests: The authors have declared that no competing interests exist.

Conducting collaborative and comprehensive epidemiological research on neonatal sepsis in low- and middle-income countries (LMICs) is challenging due to a lack of diagnostic tests. This prospective study protocol aims to obtain epidemiological data on bacterial sepsis in newborns and young infants at Kamuzu Central Hospital in Lilongwe, Malawi. The main goal is to determine if the use of whole blood transcriptome host immune response signatures can help in the identification of infants who have sepsis of bacterial causes. The protocol includes a detailed clinical assessment with vital sign measurements, strict aseptic blood culture protocol with state-of-the-art microbial analyses and RNA-sequencing and metagenomics evaluations of host responses and pathogens, respectively. We also discuss the directions of a brief analysis plan for RNA sequencing data. This study will provide robust epidemiological data for sepsis in neonates and young infants in a setting where sepsis confers an inordinate burden of disease., Competing Interests: Competing interests: The investigators in this study are academic researchers in the public sector. Investigators in Malawi receive salary compensation for their time invested carrying this study. Many investigators on this study are clinicians involved in caring for sick babies and have a deep, vested interest in generating new knowledge as well as ensuring safety and well-being of research participants., (Copyright: © 2020 Popescu CR et al.)

Purpose: Peripheral nerve blocks (PNBs) provide excellent perioperative analgesia but can increase the risk of severe postoperative pain once the block wears off. Poor adherence to discharge instructions may increase this risk. Panda-Nerve Block (Panda) is an app that alerts the patient to assess their PNB, score their pain, and take scheduled pain medication. We assessed the usability and feasibility of Panda for assisting patients after receiving a PNB., Methods: Twenty-nine patients tested Panda in three rounds, for two to seven days, postoperatively to assess and manage their pain and PNB. Feedback was provided via phone interview and the Computer System Usability Questionnaire (CSUQ). Additionally, each user's usage log was analyzed for parameters such as alert response times. Feasibility was determined by alert responses that occurred before the next alert, with a goal of greater than 50%. User adherence was measured as percentage compliance with alerts within one hour; usability and user satisfaction were determined from the CSUQ and interviews., Results: A median [interquartile range (IQR)] of 68 [34-93]% responded before the next alert during the first 48 hr of app use, and 83 [54-92]% responded before the next alert with 87 [75-96]% of these within one hour. There were no significant differences in usage between rounds. Ninety-three percent of patients reported Panda to be easy to use and helpful, and 79% of patients would use Panda again. Critical themes included changes to the layout and appearance, clarification of the language of the PNB check, and requests for dynamic adjustments to the medication schedule based on user responses., Conclusion: Panda-Nerve Block is a feasible method for PNB patients to manage postoperative pain with a high response rate. Future work should include providing two-way communication for patients and clinicians and assessing its effect on pain outcomes., Trial Registration: www.clinicaltrials.gov (NCT03369392); registered 5 December 2017.

Background: Sepsis is the leading cause of death and disability in children. Every hour of delay in treatment is associated with an escalating risk of morbidity and mortality. The burden of sepsis is greatest in low- and middle-income countries where timely treatment may not occur due to delays in diagnosis and prioritization of critically ill children. To circumvent these challenges, we propose the development and clinical evaluation of a digital triage tool that will identify high risk children and reduce time to treatment. We will also implement and clinically validate a Radio-Frequency Identification system to automate tracking of patients. The mobile platform (mobile device and dashboard) and automated patient tracking system will create a low cost, highly scalable solution for critically ill children, including those with sepsis., Methods: This is pre-post intervention study consisting of three phases. Phase I will be a baseline period where data is collected on key predictors and outcomes before implementation of the digital triage tool. In Phase I, there will be no changes to healthcare delivery processes in place at the study hospitals. Phase II will involve model derivation, technology development, and usability testing. Phase III will be the intervention period where data is collected on key predictors and outcomes after implementation of the digital triage tool. The primary outcome, time to treatment initiation, will be compared to assess effectiveness of the digital health intervention., Discussion: Smart technology has the potential to overcome the barrier of limited clinical expertise in the identification of the child at risk. This mobile health platform, with sensors and data-driven applications, will provide real-time individualized risk prediction to rapidly triage patients and facilitate timely access to life-saving treatments for children in low- and middle-income countries, where specialists are not regularly available and deaths from sepsis are common., Trial Registration: Clinical Trials.gov Identifier: NCT04304235, Registered 11 March 2020.

Background: counting respiratory rate over 60 seconds can be impractical in a busy clinical setting., Methods: 870 respiratory rates of 272 acutely ill medical patients estimated from observations over 15 seconds and those calculated by a computer algorithm were compared., Results: The bias of 15 seconds of observations was 1.85 breaths per minute and 0.11 breaths per minute for the algorithm derived rate, which took 16.2 SD 8.1 seconds. The algorithm assigned 88% of respiratory rates their correct National Early Warning Score points, compared with 80% for rates from 15 seconds of observation., Conclusion: The respiratory rates of acutely ill patients are measured nearly as quickly and more reliably by a computer algorithm than by observations over 15 seconds.

Competing Interests: The authors are the inventors of the RRate app. that has been evaluated by Baker et al.

Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age. Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers. Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation.  Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice., Competing Interests: No competing interests were disclosed., (Copyright: © 2019 Mawji A et al.)

Background: Emphasis on outpatient pediatric surgical procedures places the burden of responsibility for postoperative pain management on parents or guardians. Panda is a mobile phone app that provides scheduled medication alerts and allows parents to track their child's pain and medication administration. We have previously tested and optimized the usability and feasibility of Panda within the hospital setting., Objective: The purpose of this study was to evaluate and optimize the usability and feasibility of Panda for use at home based on alert response adherence (response to any medication notification within 1 hour) and parents' satisfaction., Methods: Parents or guardians of children aged 3 to 18 years undergoing day surgery were recruited to use Panda at home for 1 to 7 days to manage their scheduled medications and to assess their child's pain. After the surgical procedure, a research assistant guided parents through app setup before independent use at home. We aimed to recruit 10 child-caregiver pairs in each of three rounds of evaluation. Each user's adherence with the recommended medication alerts was analyzed through audit-trail data generated during the use of the app. We used the Computer System Usability Questionnaire and a poststudy phone interview to evaluate the app's ease of use and identify major barriers to adoption. Suggestions provided during the interviews were used to improve the app between each round., Results: Twenty-nine child-caregiver pairs participated in three rounds, using the app for 1 to 5 days. Alert response adherence (response to any medication notification within 1 hour) improved as the study progressed: participants responded to a median 30% (interquartile range [IQR] 22%-33%) of alerts within 1 hour in round 1, and subsequently to median 60% (IQR 44%-64%) in round 2 and median 64% (IQR 56%-72%) in round 3 (P=.005). Similarly, response times decreased from median 131 (IQR 77-158) minutes in round 1 to median 31 (IQR 18-61) minutes in round 2 and median 10 (IQR 2-14) minutes in round 3 (P=.002). Analysis of interview feedback from the first two rounds revealed usability issues, such as complaints of too many pages and trouble hearing app alerts, which were addressed to streamline app function, as well as improve visual appearance and audible alerts., Conclusions: It is feasible for parents or guardians to use Panda at home to manage their child's medication schedule and track their pain. Simple modifications to the app's alert sounds and user interface improved response times., (©Dustin Dunsmuir, Helen Wu, Terri Sun, Nicholas C West, Gillian R Lauder, Matthias Görges, J Mark Ansermino. Originally published in JMIR Perioperative Medicine (http://periop.jmir.org), 04.07.2019.)

Background: Respiratory rate is often measured over a period shorter than 1 min and then multiplied to produce a rate per minute. There are few reports of the performance of such estimates compared with rates measured over a full minute., Aim: Compare performance of respiratory rates calculated from 15 and 30 s of observations with measurements over 1 min., Design: A prospective single center observational study., Methods: The respiratory rates calculated from observations for 15 and 30 s were compared with simultaneous respiratory rates measured for a full minute on acutely ill medical patients during their admission to a resource poor hospital in sub-Saharan Africa using a novel respiratory rate tap counting software app., Results: There were 770 respiratory rates recorded on 321 patients while they were in the hospital. The bias (limits of agreement) between the rate derived from 15 s of observations and the full minute was -1.22 breaths per minute (bpm) (-7.16 to 4.72 bpm), and between the rate derived from 30 s and the full minute was -0.46 bpm (-3.89 to 2.97 bpm). Rates observed over 1 min that scored 3 National Early Warning Score points were not identified by half the rates derived from 15 s and a quarter of the rates derived from 30 s., Conclusion: Practice-based evidence shows that abnormal respiratory rates are more reliably detected with measurements made over a full minute, and respiratory rate measurement 'short-cuts' often fail to identify sick patients., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Association of Physicians. All rights reserved. For permissions, please email: journals.permissions@oup.com.)